TrafficSense AI: Design and Implementation of an AI-Driven Predictive Framework for Adaptive Urban Traffic Signal Contr

Author(s)	Anbarasan S, Deepak Kumaran V, Aravindhan G, Gowtham R, Vasuki S
Country	India
Abstract	Escalating vehicular density in metropolitan areas has intensified traffic congestion, contributing to prolonged commute durations, excessive fuel consumption, and deteriorating air quality. Conventional signal control mechanisms — whether based on fixed timing cycles or costly embedded sensors — lack the responsiveness needed for continuously fluctuating traffic conditions. This paper introduces TrafficSense AI, a hardware-independent intelligent signal management framework leveraging AI and data-driven modeling to enhance urban traffic flow. The system integrates six functional modules: a Self-Learning Traffic Brain, Crowd Pulse Estimation, Traffic DNA Pattern System, What-If Scenario Simulator, Virtual Emergency Corridor, and Eco Mode Optimization. Simulation experiments confirm a 25–40% reduction in average vehicle waiting time and a 15–30% gain in traffic throughput over conventional fixed-time strategies.
Keywords	Adaptive Traffic Signal Control, Deep Reinforcement Learning, Sensorless Traffic Estimation, Smart City, Urban Traffic Optimization
Field	Computer Applications
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-14

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